This paper proposes a transformer-hypernetwork-controlled deep-unfolded phase-aware channel estimation refinement (THUNDER) for phase-drifting backscatter links. Residual carrier-phase drift across the pilot block renders the backscattered observation phase-nonstationary, and a closed-form phase-aware channel estimation (PACE) compensates only the first-order phase component, leaving a deterministic high signal-to-noise ratio (SNR) error floor. THUNDER suppresses this floor by initializing from PACE and refining the estimate through unfolded Gauss-Newton steps on the exact phase-exponential model. A transformer extracts pilot-wide phase context, and a hypernetwork generates bounded controls and pilot-reliability weights. Evaluations show an 8.9 dB normalized mean square error gain over the strongest learning-based channel estimation baseline.
Artificial intelligence (AI) and machine learning (ML)-based channel estimators silently degrade when propagation conditions drift from their training distributions. This letter proposes a model-agnostic cognitive digital twin (CDT) framework that combines a variational autoencod…
Integrated sensing and communications (ISAC), empowered by dynamic metasurface antennas (DMAs), has emerged as a promising paradigm for next-generation wireless networks. However, existing DMA-based designs commonly rely on the frequency-flat response model for DMA elements, whic…
Data-aided channel estimation enhances spectral efficiency by reusing detected symbols as virtual pilots. In this process, selecting only reliable symbols is crucial to prevent misdetected symbols from corrupting the channel estimate. However, conventional methods rely exclusivel…
Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems remains challenging when demodulation reference signal (DMRS) observations are sparse and noisy, and when DMRS configurations vary across deployment scenarios. This paper proposes DANCE (Diff…
The terahertz (THz) frequency band offers the potential for ultra-high data rate transmission in future wireless communication systems. To extend the transmission distance and enhance spectral efficiency, the deployment of large-scale antenna arrays emerges as a promising solutio…
Attention-based neural estimators achieve strong channel-estimation accuracy, but the computational cost of global attention over the time-frequency resource grid grows quadratically with the number of subcarriers, and these estimators are typically tied to a single resource allo…